Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series

Maruotti, Antonello, Punzo, Antonio and Bagnato, Luca (2018) Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series. Journal of Financial Econometrics, 17 (1), 91-117. (doi:10.1093/jjfinec/nby019).

Record type: Article

Abstract

We introduce multivariate models for the analysis of stock market returns. Our models are developed under hidden Markov and semi-Markov settings to describe the temporal evolution of returns, whereas the marginal distribution of returns is described by a mixture of multivariate leptokurtic-normal (LN) distributions. Compared to the normal distribution, the LN has an additional parameter governing excess kurtosis and this allows us a better fit to both the distributional and dynamic properties of daily returns. We outline an expectation maximization algorithm for maximum likelihood estimation which exploits recursions developed within the hidden semi-Markov literature. As an illustration, we provide an example based on the analysis of a bivariate time series of stock market returns.

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